Credibility support vector machines based on fuzzy outputs
Support vector machines (SVMs) based on fuzzy theory have attracted widespread attentions in pattern recognition and machine learning. However, these SVMs have some limitation in dealing with some classification problems with fuzzy outputs, which results in the ignorance of the fuzziness of fuzzy outputs. Motivated by this, the possibility and necessity of fuzziness of fuzzy outputs are discussed, and the dynamic partitioning methods of these fuzzy output training samples are demonstrated based on credibility measure. Then, the corresponding dynamic credibility support vector machines based on fuzzy outputs are established, and the feasibility and effectiveness of credibility SVMs are shown by experimental results.
KeywordsSupport vector machines Fuzzy outputs Credibility measure Confidence levels
This work was supported by the National Natural Science Foundation of China (No. 11626079), the Natural Science Foundation of Hebei Province of China (No. F2015402033), and Scientific Research Project of Higher Education Institutions of Hebei Province(No. BJ2017031).
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
- Mozafari AS, Jamzad M (2016) A svm-based model-transferring method for heterogeneous domain adaptation. Expert Syst Appl 56:142–158Google Scholar
- Yang ZM, Deng NY (2007) Fuzzy support vector classification based on possibility theory. Pattern Recog Artif Intell 20(1):7–14Google Scholar
- Yang ZM, Liu GL (2012) Uncertainty support vector machines: algorithms and applications. Science Press, BeijingGoogle Scholar